RUL Prediction with Joint Training Under Sparse Time-Series Data
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Solution Overview
Problem
Current methods for predicting the remaining useful life (RUL) of industrial equipment face challenges due to insufficient accurate time-series data, lack of explainability in machine learning models, and the potential for overly conservative or aggressive predictions, leading to costly maintenance actions and downtime.
Innovation Solution
A Generative Adversarial Network (GAN) based Jointly Trained Health Predictor (HP-JT) method that generates synthetic degradation data to enhance the learning and forecast performance of a health predictor, allowing for accurate RUL predictions even with limited training data, and providing explainable predictions through the integration of a neural network layer, such as LSTM, within the GAN architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for RUL prediction, then prediction capability is provided, but prediction accuracy deteriorates due to insufficient training data
Solution Approach 1:
The patent applies data augmentation by generating synthetic degradation trajectories that copy and extend real degradation patterns. The GAN framework creates artificial but realistic degradation data that mimics the statistical properties and degradation modes of actual equipment, effectively multiplying the available training data without requiring additional physical experiments or equipment failures.
Solution Approach 2:
The patent performs preliminary degradation through synthetic data generation before actual prediction tasks. By pre-generating diverse degradation trajectories using the GAN model trained on limited real data, the system prepares a comprehensive training dataset in advance that covers various failure modes and degradation patterns, enabling more robust predictions when actual equipment is monitored.
2Reliability
If machine learning models provide predictions, then prediction capability is achieved, but explainability deteriorates due to black-box nature
Solution Approach 1:
The patent introduces an explainability module as an intermediary between the GAN-based prediction model and the user. This module translates the black-box predictions into interpretable insights by identifying which degradation features and patterns most influenced the RUL prediction, providing context without requiring changes to the core predictive model architecture.
3Reliability
If conservative predictions are made to ensure safety, then worker safety is improved, but productivity deteriorates due to premature maintenance
Solution Approach 1:
The patent changes the output parameters of the prediction model to provide not just a single RUL estimate but also confidence intervals, degradation rates, and failure probability distributions. This allows users to adjust the conservatism level based on their risk tolerance and operational requirements, transforming safety from a binary constraint into a tunable parameter.
4Productivity
If aggressive predictions are made to prevent downtime, then productivity is improved, but reliability deteriorates due to missed failures
Solution Approach 1:
The patent implements feedback mechanisms where prediction confidence levels and uncertainty metrics are continuously monitored and fed back into the maintenance decision-making process. When prediction uncertainty is high, the system automatically adjusts maintenance scheduling and may trigger additional inspections, creating a closed-loop system that balances productivity gains with reliability safeguards.
Data Source
AI summary
A method for using time-series data to predict remaining useful life of industrial equipment in cases where limited time-series training data is available is described. The method includes steps of monitoring the industrial equipment to sense historical time-series data associated with the industrial equipment using at least one sensor, storing the historical time-series data from the at least one sensor, accessing the historical time-series data and pre-processing the historical time-series data to extract higher-level features associated with the remaining useful life of the industrial equipment, and applying a jointly trained health predictor to the higher-level features using a computing device by executing a set of instructions from a non-transitory machine readable memory using a processor of the computing device to determine a prediction for the remaining useful life of the industrial equipment.